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Sunday, November 6, 2016

Estimating the Effects of Brownfields and Brownfield Remediation on Property Values in a New South City

Abstract:

Using data from Charlotte, NC, a New South city without a legacy of heavily contaminated properties, we find the distance from unremediated brownfields—typically former industrial properties believed to have modest contamination—to have no effect on residential sales values, but proposed cleanup and actual remediation have positive, substantial, and significant effects especially within 0.5 miles of the brownfield. Our results are consistent whether we examine all property values within a given distance, such as 0.5 miles, or examine discrete distances, such as 0.3–0.5 miles. An estimate of the benefits is on the order of $4 million.

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In an abstract available free of charge at http://tinyurl.com/jbqyqauthe authors report that the median brownfield size ... is nearly 262,000 square feet.,,, Based on tables 2 and 3, a 10,000 square foot increase in the size of a brownfield results in a decrease in property value of about 3%. For an otherwise comparable house valued at the median of $95,000 (adjusted using a 1995 price index), the house near the brownfield loses almost $3,000 in value for each 10,000 square foot increase in the size of the brownfield.

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If stage 2 is reached where a developer applies for constructive notice, as is the case for about 40% of the transactions in this sample, a house within 0.5 miles from the brownfield experiences an increase in value, with the size of the increase decreasing with the size of the brownfield. For the median size brownfield of 262,000 sq. ft., the net effect is on the order of 0.3 – 26.2*(0.005) = 0.17 or 17%. The $95,000 median house would now be worth $111,000 given the developer intentions, a gain of almost $16,000. For our data sample of approximately 500 houses within 0.5 miles of a given brownfield and based on median values, 200 homes (40% of 500) will gain the premium for an overall gain of around $3,230,000. We attribute these benefits to the market expectation that a developer applying to the brownfields program expects to remediate and eventually redevelop.

The benefits of the brownfields program increase further where there is actual remediation (and would increase again with redevelopment). Again choosing the 0.5 mile distance, the lift to property value is a little over 10% (bearing in mind that homeowners out to 2 miles will benefit, and homeowners within 0.3 miles are actually worse off). The house that had already appreciated by almost $16,000 would now add approximately $11,000 to $122,300. Approximately 15% of sales took place after remediation. For the sample of 500 houses within a 0.5 mile of the nearest brownfield, there is an additional $10,000 in benefits for the 75 homes that sell after remediation, or an additional $830,000, bringing the overall gain to $4.1 million.

The eventual gain will likely be larger, as a longer time period would allow more developers to remediate (and then a fourth stage of benefits when they redevelop). These results are larger than those found in similar studies (Linn, 2012, Kaufman and Cloutier 2005). One possible explanation is that Charlotte’s brownfields are mildly contaminated as compared to cities more dependent on heavy industry. So brownfields in our sample have less of a detrimental effect, and there may be a higher premium for developer notice given the greater likelihood that contamination will be mild, making it more likely that remediation and redevelopment will occur. Another possibility is that we have not completely accounted for two sources of upward bias given by Linn (2013). First, Linn suggests the (dis)amenity effect. Houses near brownfields tend to be in industrial areas with lower quality amenities (e.g. low-quality schools). So the reduction in property values attributed to brownfields is biased upward, because part of the reduction is due to lower amenities, if those amenities are not captured by variables for neighborhood characteristics. We include census tracts, but their inclusion may not do as good of a job of accounting for (dis) amenities as Linn’s block measure, which is considerably smaller than a census tract. He also interacts his block measure with other variables, but we are unable to do so given our much smaller sample size, as compared to Linn. The second bias is the possibility of reverse causality. We may be attributing too much of the increase in property values to developer actions. The increase in property values may be exogenous to some degree, and the developer response to those rising values endogenous. Linn suggests including a pre-sample median house price interacted with a time trend as a way to counter this simultaneity. Again, our smaller sample size limited our ability to add a large number of additional variables. Instead, we use knowledge of Charlotte’s development trends to isolate certain potentially problematic brownfields and tested the sensitivity of our results with and without these brownfields, as discussed in Appendix 2

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